Paramils

Paramils: an automatic algorithm configuration framework. The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.


References in zbMATH (referenced in 72 articles , 1 standard article )

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  1. Montero, Elizabeth; Riff, María-Cristina: On-the-fly calibrating strategies for evolutionary algorithms (2011) ioport
  2. Tompkins, Dave A. D.; Balint, Adrian; Hoos, Holger H.: Captain Jack: new variable selection heuristics in local search for SAT (2011)
  3. Hutter, Frank; Bartz-Beielstein, Thomas; Hoos, Holger H.; Leyton-Brown, Kevin; Murphy, Kevin P.: Sequential model-based parameter optimization: an experimental investigation of automated and interactive approaches (2010)
  4. Hutter, Frank; Hoos, Holger H.; Leyton-Brown, Kevin: Tradeoffs in the empirical evaluation of competing algorithm designs (2010) ioport
  5. Hutter, Frank; Hoos, Holger H.; Leyton-Brown, Kevin: Automated configuration of mixed integer programming solvers (2010) ioport
  6. Ke, Liangjun; Feng, Zuren; Ren, Zhigang; Wei, Xiaoliang: An ant colony optimization approach for the multidimensional knapsack problem (2010)
  7. Maturana, Jorge; Lardeux, Frédéric; Saubion, Frédéric: Autonomous operator management for evolutionary algorithms (2010)
  8. Tompkins, Dave A. D.; Hoos, Holger H.: Dynamic scoring functions with variable expressions: new SLS methods for solving SAT (2010)
  9. Vasilikos, Vasileios; Lagoudakis, Michail G.: Optimization of heuristic search using recursive algorithm selection and reinforcement learning (2010)
  10. Birattari, Mauro: Tuning metaheuristics. A machine learning Perspective (2009)
  11. Burke, Edmund K.; Hyde, Mathew R.; Kendall, Graham; Ochoa, Gabriela; Ozcan, Ender; Woodward, John R.: Exploring hyper-heuristic methodologies with genetic programming (2009)
  12. Hutter, F.; Hoos, H. H.; Leyton-Brown, K.; Stuetzle, T.: Paramils: an automatic algorithm configuration framework (2009)